We compared Apache Kafka and Amazon SQS based on our user's reviews in several parameters.
Apache Kafka stands out for its high scalability, fault-tolerant architecture, real-time data handling, stream processing, and data replication support. On the other hand, Amazon SQS is praised for its reliability, scalability, and ability to decouple application components seamlessly. While Apache Kafka offers easy integration with programming languages and frameworks, Amazon SQS provides efficient message handling for large volumes. Overall, Apache Kafka focuses on real-time data processing and stream processing, while Amazon SQS emphasizes reliable message handling and decoupling application components.
Features: Apache Kafka is highly valued for its high scalability, fault-tolerant architecture, and support for real-time data handling. It also offers seamless integration with programming languages and frameworks, and functionalities like stream processing and data replication. On the other hand, Amazon SQS is highly appreciated for its reliability, scalability, and the ability to decouple different components of an application, allowing for seamless integration and flexibility. It efficiently handles large volumes of messages.
Pricing and ROI: The available data did not provide any information about the setup cost for Apache Kafka. There were no details about the pricing, setup cost, and licensing for Amazon SQS from the reviewers., The ROI reviews for Apache Kafka are missing or unavailable, while for Amazon SQS, they are not available.
Room for Improvement: Apache Kafka: No specific feedback is available regarding areas for improvement. Amazon SQS: No specific feedback or suggestions have been provided for improvement.
Deployment and customer support: The given data source does not provide any user feedback specifically about the duration required to establish a new tech solution for Apache Kafka. Similarly, there is no specific information or quotes available regarding the setup time for Amazon SQS., Customer service and support for Apache Kafka cannot be compared as no reviews or feedback are available. Similarly, there are no reviews for customer service of Amazon SQS.
The summary above is based on 46 interviews we conducted recently with Apache Kafka and Amazon SQS users. To access the review's full transcripts, download our report.
"The libraries that connect and manage the queues are rich in features."
"It is stable and scalable."
"SQS is very stable, and it has lots of features."
"We use the tool in interface integrations."
"There is no setup just some easy configuration required."
"I appreciate that Amazon SQS is fully integrated with Amazon and can be accessed through normal functions or serverless functions, making it very user-friendly. Additionally, the features are comparable to those of other solutions."
"It's very quick and easy to build or set up Amazon SQS."
"The most valuable feature of Amazon SQS is the interface."
"The open-source version is relatively straightforward to set up and only takes a few minutes."
"Apache Kafka's most valuable features include clustering and sharding...It is a pretty stable solution."
"Its availability is brilliant."
"The most valuable features are the stream API, consumer groups, and the way that the scaling takes place."
"All the features of Apache Kafka are valuable, I cannot single out one feature."
"We get amazing throughput. We don't get any delay."
"Deployment is speedy."
"It is the performance that is really meaningful."
"Be cautious around pay-as-you-use licensing as costs can become expensive."
"There are some issues with SQS's transaction queue regarding knowing if something has been received."
"The tool needs improvement in user-friendliness and discoverability."
"Support could be improved."
"I cannot send a message to multiple people simultaneously. It can only be sent to one recipient."
"The solution is not available on-premises so that rules out any customers looking for the messaging solution on-premises."
"I do not think that this solution is easy to use and the documentation of this solution has a lot of problems and can be improved in the next release. Most of the time, the images in the document are from older versions."
"Sending or receiving messages takes some time, and it could be quicker."
"Kafka requires non-trivial expertise with DevOps to deploy in production at scale. The organization needs to understand ZooKeeper and Kafka and should consider using additional tools, such as MirrorMaker, so that the organization can survive an availability zone or a region going down."
"There is a lot of information available for the solution and it can be overwhelming to sort through."
"I would like to see monitoring service tools."
"Observability could be improved."
"Kafka is complex and there is a little bit of a learning curve."
"I would like them to reduce the learning curve around the creation of brokers and topics. They also need to improve on the concept of the partitions."
"Apache Kafka can improve by adding a feature out of the box which allows it to deliver only one message."
"We cannot apply all of our security requirements because it is hard to upload them."
Amazon SQS is ranked 4th in Message Queue (MQ) Software with 12 reviews while Apache Kafka is ranked 1st in Message Queue (MQ) Software with 76 reviews. Amazon SQS is rated 8.0, while Apache Kafka is rated 8.0. The top reviewer of Amazon SQS writes "Stable, useful interface, and scales well". On the other hand, the top reviewer of Apache Kafka writes "Real-time processing and reliable for data integrity". Amazon SQS is most compared with Redis, Amazon MQ, Anypoint MQ, IBM MQ and Oracle Event Hub Cloud Service, whereas Apache Kafka is most compared with IBM MQ, Red Hat AMQ, Anypoint MQ, PubSub+ Event Broker and VMware RabbitMQ. See our Amazon SQS vs. Apache Kafka report.
See our list of best Message Queue (MQ) Software vendors.
We monitor all Message Queue (MQ) Software reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
